AI Use-Cases for the Future of Real Estate

Raghav serves as Content Lead at Emerj, covering our major industry areas and conducting research. Raghav has a personal interest in robotics, and previously worked for research firms like Frost & Sullivan and Infiniti Research.

Episode summary: In this episode of AI in Industry, we speak with Andy Terrel, the Chief Data Scientist at REX – Real Estate Exchange Inc., about how AI is being used in the real estate sector today.

Looking ahead ten years into the future, Andy paints a picture of the areas where he believes AI will change the real estate business. Andy explores how marketing in real estate might change in the future with chatbots and conversational interfaces in real estate which are high value per ticket interactions – a process that will likely vary greatly from the chatbot applications we see for smaller B2C purchases (in the fashion sector, eCommerce, etc).

Brief recognition: Andy earned a bachelor’s degree in Physics and Mathematics from Texas Tech University in 2004. He went on to earn a master’s degree and a Ph.D. in Computer Science from the University of Chicago. He was also a Research Associate Scientist at the University of Texas before serving as the Chief Science Officer for Continuum Analytics and the Chief Technology Officer for Bold Metrics Inc. He has served as Chief Data Scientist at REX since 2017.

Current Affiliations: Advisor at KindHealth, President at NumFOCUS Foundation and Chief Data Scientist at REX.

Big Idea

Andy tells us that data about real estate properties are available online including information like transactions undertaken over a particular home, features and amenities available in a specific home, and more (much of this publicly available data on the MLS is already pulled in and aggregated by sites like Zillow or RedFin).

He states that using a conversational interface to answer questions that prospective buyers or sellers of a real estate property may have can be distilled down to around 60 – 75 most commonly asked questions.

Questions might require a simple reply, such as:

“Does the house have a pool?”

“How many bedrooms does this house have?”

But more complex questions might be asked of such a system as well:

“How much money will I have to spend to fix the roof?”

“How many cars fit in the garage?”

Andy explains that answering these more complex questions at scale is a task which is difficult and time consuming for humans to achieve efficiently. While it’s clear that REX hasn’t achieved a full Q-and-A solution for real estate, Andy believes that such a system could eventually be a normal part of the real estate shopping and buying process.

Andy predicts that in the future AI might be useful for more than simply answering a question – it might be helpful in identifying the right contextual questions to ask. For example, information about how old a roof is can potentially lead to more residual information like how old the house was or how well maintained the house was by the previous owners.

Interview Highlights with Andy Terrel from REX

The main questions Andy answered on this topic are listed below. Listeners can use the embedded podcast player (at the top of this post) to jump ahead to sections they might be interested in:

(2:57) What’s possible with AI today in the real estate sector?

(5:24) What kind of data is collected to aid in finding answers to the more complex questions from customers?

(9:08) What role does AI play in marketing for the real estate sectors?

(11:21) What are the channels of matching listing variables with buyer/seller variables?

(19.20) What are some of the challenges with getting conversational interfaces to click in this space?

Episode Summary: Big data is often a buzz word, but if you're trying to quantify data around homes in the U.S. and pair that with hard to quantify information - like images - you're likely running into the frontiers of machine learning technology. This is something Zillow deals with daily. In this episode, Stan Humphries, chief analytics officer and economist for Zillow, speaks about where they're leveraging machine learning and artificial intelligence (hint: almost everywhere), and what he believes are the keys for deriving real ROI opportunities using this technology. Humphries also offers insights for how other companies can model the successful decision-making processes and implementation strategies used by Zillow.

Episode Summary: In this episode, we speak with Dr. Matteo Berlucchi, the founder of Your.MD, which uses artificial intelligence to create one of the first personal health assistant platforms in 70+ countries. Berlucchi talks about the challenges in making an AI do what you want, specifically helping people self diagnose and seek proper treatment. He discusses the multiple approaches to AI that are blended together in order to yield optimal results, and touches on the sometimes stark differences between what AI can do in the lab versus the functional application for tens of thousands of people. If you're interested in the diverse applications of AI and the challenges in running a startup, Dr. Berlucchi's makes for an interesting episode.

Episode summary: In this episode we talk to Adam Spector, the Co-Founder & Chief Business Officer at LiftIgniter, a company which provide a service which modulates website experience per users, for an array of different businesses. Adam and I discuss what the tech giants are doing to customize their business experiences, what data they’re using to continually alter user experience and what industries and sectors might be impacted by this aggregate trend as it moves forward.

Stay Ahead of the Machine Learning Curve

At Emerj, we have the largest audience of AI-focused business readers online - join other industry leaders and receive our latest AI research, trends analysis, and interviews sent to your inbox weekly.